demystifying healthcare data governance

© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright


Dales Sanders – May 7, 2014

Demystifying Healthcare Data Governance

© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 2

Today’s Agenda

General concepts in data governance

Unique aspects of data governance in healthcare

The layers and roles in data governance

Constant theme: Data governance as it relates to analytics and data warehousing


A Sampling of My Up & Down Journey

TOO LITTLE DATA GOVERNANCE

TOO MUCH DATA GOVERNANCE

WWMCCS: Worldwide Military Command & Control SystemMMICS: Maintenance Management Information Collection SystemNSA: National Security AgencyIMDB: Integrated Minuteman Data BasePIRS: Peacekeeper Information Retrieval SystemEDW: Enterprise Data Warehouse

(1986)WWMCCS

(1987)MMICS

(1992)NSA ThreatReporting

●

● ●● ●

●

●

●

(1995)IMDB

& PIRS

(1996)IntelLogisticsEDW

(1998)Intermountain

Healthcare

(2005)Northwestern

EDW

(2009)Cayman

Islands HSA

1983

2014


The Sanders Philosophy of Data Governance

The best data governance governs to the least extent necessary to achieve the greatest common good.”

Govern no data until its time.”


Centralized EDW; monolithic early

binding data model

Data Governance Cultures

HIGHLY CENTRALIZED GOVERNMENT

BALANCED GOVERNMENT

HIGHLY DECENTRALIZED GOVERNMENT

AUTHORITARIAN DEMOCRATIC TRIBAL

Centralized EDW; distributed late

binding data model

No EDW; multiple, distributed analytic

systems


Characteristics of Democracy

Elements of centralized decision making● Elected or appointed, centralized representatives

● Majority rules

Elements of decentralized action● Direct voting and participation, locally

● Everyone is expected to participate in developing shared values, rules, and laws; then abide by them and act accordingly


What’s It Look Like?

Not enough data governance Completely decentralized, uncoordinated data analysis

resources-- human and technology

Inconsistent analytic results from different sources, attempting to answer the same question

Poor data quality, e.g., duplicate patient records rate is > 10% in the master patient index

When data quality problems are surfaced, there is no formal body nor process for fixing those problems

Inability to respond to new analytic use cases and requirements… like accountable care


What’s It Look Like?

Too much data governance Unhappy data analysts… and their customers

Everything takes too long

– Loading new data

– Making changes to data models to support new analytic use cases

– Getting access to data

– Resolving data quality problems

– Developing new reports and analyses


Poll Question

What best describes the current state of affairs for data governance in your organization?

193 Respondents

Authoritarian – 19.7%

Democratic – 24.3%

Tribal – 56%


Poll Question

How would you rate data governance effectiveness in your organization?

179 Respondents

5 – Very effective – 1.6%

4 – 7.2%

3 – 22.3%

2 – 44.1%

1 – Ineffective – 24.8%


The Triple Aim of Data Governance

1. Ensuring Data Quality• Data Quality = Completeness x Validity

2. Building Data Literacy in the organization• Hiring and training to become a data driven company

3. Maximizing Data Exploitation for the organization’s benefit• Pushing the data-driven agenda for cost reduction,

quality improvement, and risk reduction


Keys to Analytic Success

The Data Governance Committee should be a driving force in all three…

– Setting the tone of “data driven” for the culture

– Actively building and recruiting for data literacy among employees

– Choosing the right kind of tools to support analytics and data governance

Mindset

Skillset

Toolset


The Data Governance Layers

Happy Data Analyst


The Different Roles in Each Layer

Executive & Board Leadership

We need a longitudinal analytic view across the ACO of a patient’s treatment and costs, as well as all similar patients in the population we serve.”



Data Governance Committee

We need an enterprise data warehouse that contains all of the clinical data and financial data in the ACO, as well as a master patient identifier.”

We need a data analysis team, as well as the IT skills to manage a data warehouse.”

The following roles in the organization should have the following types of access to the EDW.”



Data Stewards

I’m responsible for patient registration. I can help.”

I’m responsible for clinical documentation in Epic. I can help.”

I’m responsible for revenue cycle and cost accounting. I can help.”



Data Architects & Programmers

We will extract and organize the data from the registration, EMR, rev cycle, and cost accounting and load it into the EDW.”

“Data stewards, can we sit down with you and talk about the data content in your areas?”

“DBAs and Sys Admins, here are the roles and access control procedures for this data.”



DBAs & System Administrators

Here is the access control list and procedures for approving access to this data. Let’s build the data base roles and audit trails to support these.”



Data access & control system

When this person logs in, they have the following rights to create, read, update, and delete this data in the EDW.”



Data Analysts

I’ll log into the EDW and build a query against the data in the EDW that should be able to answer these types of questions.”

“Data Stewards, can I cross check my results with you to make sure I’m pulling the data properly?”

“Data architects, I’ll let you know if I have any trouble with the way the data is organized or modeled.”


Who Is On The Data Governance Committee?

Representing the analytics customers

The data technologist

The clinical data owners

The financial and supply chain data owner

Representing the researchers’ data needs

Chief Analytics Officer

CIO

CMO & CNO

CFO

CRO


Data Governance Committee Failure Modes

Wandering: Lacking direction and experience

● “We know we need data governance, but we don’t know how to go about it.”

Technical Overkill: An overly passionate and inexperienced IT person leads the data governance committee

● Can’t see the forest for the trees

● For example, Executives on the Data Governance Committee (DGC) are asked to define naming conventions and data types for a database column

Politics: Members of the DGC are passive aggressive, narrowly motivated, data poseurs

● They pretend to be data driven and selfless, but they aren’t

● Territorial and defensive about “their” data

● “That person isn’t smart enough to use my data properly.”

Red Tape: Committee members are not governors of the data, they are bureaucrats

● Red tape processes for accessing data

● Confuse data governance with data security


Poll Question

Your organization’s biggest risks to the success of the Data Governance Committee

182 Respondents – Multiple Choice

Wandering – 52%

Politics – 61%

Technical Overkill – 20%

Red Tape – 36%

Other – 16%


Data Governance & Data Security

Data Governance Committee: Constantly pulling for broader data access and more data transparency

Information Security Committee: Constantly pulling for narrower data access and more data protection

Ideally, there is overlapping membership that helps with the balance

24


Tools for Data GovernanceData quality reports

– Data Quality = Validity x Completeness

CRM tools for the data warehouse– Who’s using what data? When? Why?

“White Space” data management tools– For capturing and filling-in computable data that’s missing in the

source systems

Metadata repository– What’s in the data warehouse?– Are there any data quality problems?– Who’s the data steward?– How much data is available and over what period of time?– What’s the source of the data?

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Practice

Protocols

Processing

EDWAnalyzable data

Clinicians use diverse protocols & orders in

daily care

Sub-Optimal State

The Four Levels of Closed Loop Analytics in Healthcare

© 2014 Denis Protti, Dale Sanders & Corinne Eggert

CDS:EDW:EHR:MTTI:

Clinical Decision SupportEnterprise Data WarehouseElectronic Health Record Mean Time To Improvement

Clinical Information SystemsDecisions & ActionsSupporting information

Clinical, EHR, EDW & Analytics Teams

Align metrics & data

Update EHR & EDW with new data items if needed & possible

Start here

Monitor baselines & clinical processes

Select a problem

Set outcomes & metrics

Quality Governance

Clinical Variations & Needs

Internal EvidenceClinicians’ suggestions

External EvidenceLiterature, reports, etc.

Quality Governance

Use comparative data to identify best outcomes

Determine standard order sets, protocols & decision support rules

External EvidenceLiterature, reports, etc.

Analyze data quality & process/outcome variationsGenerate the internal evidence

Clinical Analytics

Other Data SourcesClinical, Financial, etc.

MTTILo Hi

EHR & CDSElectronic clinical data

Clinicians use standard protocols & orders

in daily care

Optimal State

Clinical, EHR, EDW & Analytics Teams

Update EHR protocols & EDW metrics

Enterprise Clinical Teams Act on performance

information

Executive & Clinical Leadership

Set expectations for use of evidence & standards

Best EvidenceInformation that clinicians trust

Stan

dard

s

Performance

26


Healthcare Analytics Adoption Model

Level 8

Level 7

Level 6

Level 5

Level 4

Level 3

Level 2

Level 1

Level 0

Personalized Medicine& Prescriptive Analytics

Clinical Risk Intervention& Predictive Analytics

Population Health Management& Suggestive Analytics

Waste & Care Variability Reduction

Automated External Reporting

Automated Internal Reporting

Standardized Vocabulary& Patient Registries

Enterprise Data Warehouse

Fragmented Point Solutions

Tailoring patient care based on population outcomes and generic data. Fee-for-quality rewards health maintenance.

Organizational processes for intervention are supported with predictive risk models. Fee-for-quality includes fixed per capita payment.

Tailoring patient care based on population metrics. Fee-for-quality includes bundled per case payment.

Reducing variability in care processes. Focusing on internal optimization and waste reduction.

Efficient, consistent production of reports & adaptability to changing requirements.

Efficient, consistent production of reports & widespread availability in the organization.

Relating and organizing the core data content.

Collecting and integrating the core data content.

Inefficient, inconsistent versions of the truth. Cumbersome internal and external reporting.

© Sanders, Protti, Burton, 2013


Progression in the Model

Data content expands– Adding new sources of data to expand our understanding of care

delivery and the patient

Data timeliness increases– To support faster decision cycles and lower “Mean Time To

Improvement”

The complexity of data binding and algorithms increases– From descriptive to prescriptive analytics– From “What happened?” to “What should we do?”

Data governance and literacy expands– Advocating greater data access, utilization, and quality

The progressive patterns at each level


Six Phases of Data Governance

You need to move through these phases in no more than two years

29

3-12 months

1-2 years

2-4 years

– Phase 6: Acquisition of Data

– Phase 5: Utilization of Data

– Phase 4: Quality of Data

– Phase 3: Stewardship of Data

– Phase 2: Access to Data

– Phase 1: Cultural Tone of “Data Driven”

Level 8

Level 1

Personalized Medicine& Prescriptive Analytics

Enterprise Data Warehouse


What Data Are We Governing?

30


Master Data Management

The data that is mastered includes:– Reference data - the dimensions for analysis– Analytical rules – supports consistent data binding

Comprises the processes, governance, policies, standards and tools that consistently define and manage the critical data of an organization to provide a single point of reference.”

- Wikipedia


Data Binding & Data Governance

“systolic &diastolicblood pressure”

Pieces ofmeaningless

data

11560

Bindsdata to

Analytics Software

Programming

Vocabulary

“normal”

Rules


Why Is This Binding Concept Important?

Data Governance needs to look for and facilitate both

33

Knowing when to bind data, and howtightly, to vocabularies and rules is

CRITICAL to analytic success and agility

Is the rule or vocabulary widely accepted as true and accurate in the organization or industry?

ComprehensiveAgreement

Is the rule or vocabulary stable and rarely change?

PersistentAgreement


Vocabulary: Where Do We Start? Charge code

CPT code

Date & Time

DRG code

Drug code

Employee ID

Employer ID

Encounter ID

Gender

ICD diagnosis code

ICD procedure code

Department ID

Facility ID

Lab code

Patient type

Patient/member ID

Payer/carrier ID

Postal code

Provider ID

In today’s environment, about 20 data elements represent 80-90% of analytic use cases. This will grow over time, but right now, it’s fairly simple.

Source data vocabulary Z (e.g., EMR)

Source data vocabulary Y (e.g., Claims)

Source data vocabulary X

(e.g., Rx)


Where Do We Start, Clinically?We see consistent opportunities, across the industry, in the following areas:

• CAUTI

• CLABSI

• Pregnancy management, elective induction

• Discharge medications adherence for MI/CHF

• Prophylactic pre-surgical antibiotics

• Materials management, supply chain

• Glucose management in the ICU

• Knee and hip replacement

• Gastroenterology patient management

• Spine surgery patient management

• Heart failure and ischemic patient management

Start Within Your Scope of InfluenceWe are still learning how to manage outpatient populations


In Conclusion

Practice democratic data governance– Find the balance between central and decentralized

governance

– Federal vs. States’ rights is a good metaphor

The Triple Aim of Data Governance– Data Quality, Data Literacy, and Data Exploitation

Analytics gives data governance something to govern– Start within your current scope of influence and data, then

grow from there


Obtain unbiased, practical, educational advice on proven analytics solutions that really work in healthcare.

The future of healthcare requires transformative thinking by committed leadership willing to forge and adopt new data-driven processes. If you count yourself among this group, then HAS ’14 is for you.

OBJECTIVE

MOBILE APPAccess to a mobile app that can be used for audience response and participation in real time. Group-wide and individual analytic insights will be shared throughout the summit, resulting in a more substantive, engaging experience while demonstrating the power of analytics.


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demystifying healthcare data governance

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